1
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Al Meslamani AZ, Sobrino I, de la Fuente J. Machine learning in infectious diseases: potential applications and limitations. Ann Med 2024; 56:2362869. [PMID: 38853633 PMCID: PMC11168216 DOI: 10.1080/07853890.2024.2362869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/02/2024] [Indexed: 06/11/2024] Open
Abstract
Infectious diseases are a major threat for human and animal health worldwide. Artificial Intelligence (AI) combined algorithms including Machine Learning and Big Data analytics have emerged as a potential solution to analyse diverse datasets and face challenges posed by infectious diseases. In this commentary we explore the potential applications and limitations of ML to management of infectious disease. It explores challenges in key areas such as outbreak prediction, pathogen identification, drug discovery, and personalized medicine. We propose potential solutions to mitigate these hurdles and applications of ML to identify biomolecules for effective treatment and prevention of infectious diseases. In addition to use of ML for management of infectious diseases, potential applications are based on catastrophic evolution events for the identification of biomolecular targets to reduce risks for infectious diseases and vaccinomics for discovery and characterization of vaccine protective antigens using intelligent Big Data analytics techniques. These considerations set a foundation for developing effective strategies for managing infectious diseases in the future.
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Affiliation(s)
- Ahmad Z. Al Meslamani
- College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates
- AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi, United Arab Emirates
| | - Isidro Sobrino
- SaBio, Instituto de Investigación en Recursos Cinegéticos (IREC), Consejo Superior de Investigaciones Científicas (CSIC), Universidad de Castilla-La Mancha (UCLM)-Junta de Comunidades de Castilla-La Mancha (JCCM), Ciudad Real, Spain
| | - José de la Fuente
- SaBio, Instituto de Investigación en Recursos Cinegéticos (IREC), Consejo Superior de Investigaciones Científicas (CSIC), Universidad de Castilla-La Mancha (UCLM)-Junta de Comunidades de Castilla-La Mancha (JCCM), Ciudad Real, Spain
- Department of Veterinary Pathobiology, Center for Veterinary Health Sciences, OK State University, Stillwater, Oklahoma, USA
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2
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Rodríguez Longarela N, Paredes Ramos M, López Vilariño JM. Bioinformatics tools for the study of bioactive peptides from vegetal sources: evolution and future perspectives. Crit Rev Food Sci Nutr 2024:1-20. [PMID: 38907628 DOI: 10.1080/10408398.2024.2367571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/24/2024]
Abstract
Bioactive peptides from vegetal sources have been shown to have functional properties as anti-inflammatory, antioxidant, antihypertensive or antidiabetic capacity. For this reason, they have been proposed as an interesting and promising alternative to improve human health. In recent years, the numerous advances in the bioinformatics field for in silico prediction have speeded up the discovery of bioactive peptides, also reducing the associated costs when using an integrated approach between the classical and bioinformatics discovery. This review aims to provide an overview of the evolution, limitations and latest advances in the field of bioinformatics and computational tools, and specifically make a critical and comprehensive insight into computational techniques used to study the mechanism of interaction that allows the explanation of plant bioactive peptide functionality. In particular, molecular docking is considered key to explain the different functionalities that have been previously identified. The assumptions to simplify such a high complex environment implies a degree of uncertainty that can only be guaranteed and validated by in vitro or in vivo studies, however, the combination of databases, software and bioinformatics applications with the classical approach has become a promising procedure for the study of bioactive peptides.
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3
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Groover KE, Randall JR, Davies BW. Development of a Selective and Stable Antimicrobial Peptide. ACS Infect Dis 2024; 10:2151-2160. [PMID: 38712889 PMCID: PMC11185160 DOI: 10.1021/acsinfecdis.4c00142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 04/17/2024] [Accepted: 04/26/2024] [Indexed: 05/08/2024]
Abstract
Antimicrobial peptides (AMPs) are presented as potential scaffolds for antibiotic development due to their desirable qualities including broad-spectrum activity, rapid action, and general lack of susceptibility to current resistance mechanisms. However, they often lose antibacterial activity under physiological conditions and/or display mammalian cell toxicity, which limits their potential use. Identification of AMPs that overcome these barriers will help develop rules for how this antibacterial class can be developed to treat infection. Here we describe the development of our novel synthetic AMP, from discovery through in vivo application. Our evolved AMP, DTr18-dab, has broad-spectrum antibacterial activity and is nonhemolytic. It is active against planktonic bacteria and biofilm, is unaffected by colistin resistance, and importantly is active in both human serum and a Galleria mellonella infection model. Several modifications, including the incorporation of noncanonical amino acids, were used to arrive at this robust sequence. We observed that the impact on antibacterial activity with noncanonical amino acids was dependent on assay conditions and therefore not entirely predictable. Overall, our results demonstrate how a relatively weak lead can be developed into a robust AMP with qualities important for potential therapeutic translation.
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Affiliation(s)
- Kyra E. Groover
- Department
of Molecular Biosciences, The University
of Texas at Austin, Austin, Texas 78712, United States
| | - Justin R. Randall
- Department
of Molecular Biosciences, The University
of Texas at Austin, Austin, Texas 78712, United States
| | - Bryan W. Davies
- Department
of Molecular Biosciences, The University
of Texas at Austin, Austin, Texas 78712, United States
- John
Ring LaMontagne Center for Infectious Diseases, The University of Texas at Austin, Austin, Texas 78712, United States
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4
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Wan F, Torres MDT, Peng J, de la Fuente-Nunez C. Deep-learning-enabled antibiotic discovery through molecular de-extinction. Nat Biomed Eng 2024:10.1038/s41551-024-01201-x. [PMID: 38862735 DOI: 10.1038/s41551-024-01201-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 03/25/2024] [Indexed: 06/13/2024]
Abstract
Molecular de-extinction aims at resurrecting molecules to solve antibiotic resistance and other present-day biological and biomedical problems. Here we show that deep learning can be used to mine the proteomes of all available extinct organisms for the discovery of antibiotic peptides. We trained ensembles of deep-learning models consisting of a peptide-sequence encoder coupled with neural networks for the prediction of antimicrobial activity and used it to mine 10,311,899 peptides. The models predicted 37,176 sequences with broad-spectrum antimicrobial activity, 11,035 of which were not found in extant organisms. We synthesized 69 peptides and experimentally confirmed their activity against bacterial pathogens. Most peptides killed bacteria by depolarizing their cytoplasmic membrane, contrary to known antimicrobial peptides, which tend to target the outer membrane. Notably, lead compounds (including mammuthusin-2 from the woolly mammoth, elephasin-2 from the straight-tusked elephant, hydrodamin-1 from the ancient sea cow, mylodonin-2 from the giant sloth and megalocerin-1 from the extinct giant elk) showed anti-infective activity in mice with skin abscess or thigh infections. Molecular de-extinction aided by deep learning may accelerate the discovery of therapeutic molecules.
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Affiliation(s)
- Fangping Wan
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Marcelo D T Torres
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Jacqueline Peng
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA.
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5
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Coelho LP, Santos-Júnior CD, de la Fuente-Nunez C. Challenges in computational discovery of bioactive peptides in 'omics data. Proteomics 2024; 24:e2300105. [PMID: 38458994 DOI: 10.1002/pmic.202300105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 02/06/2024] [Accepted: 02/06/2024] [Indexed: 03/10/2024]
Abstract
Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available 'omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.
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Affiliation(s)
- Luis Pedro Coelho
- Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology, Woolloongabba, Queensland, Australia
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
| | - Célio Dias Santos-Júnior
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
- Laboratory of Microbial Processes & Biodiversity - LMPB, Hydrobiology Department, Federal University of São Carlos - UFSCar, São Paulo, Brazil
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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6
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Santos-Júnior CD, Torres MDT, Duan Y, Rodríguez Del Río Á, Schmidt TSB, Chong H, Fullam A, Kuhn M, Zhu C, Houseman A, Somborski J, Vines A, Zhao XM, Bork P, Huerta-Cepas J, de la Fuente-Nunez C, Coelho LP. Discovery of antimicrobial peptides in the global microbiome with machine learning. Cell 2024:S0092-8674(24)00522-1. [PMID: 38843834 DOI: 10.1016/j.cell.2024.05.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 04/11/2024] [Accepted: 05/06/2024] [Indexed: 06/25/2024]
Abstract
Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental and host-associated habitats to create the AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, few of which match existing databases. AMPSphere provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences, and we observed that AMP production varies by habitat. To validate our predictions, we synthesized and tested 100 AMPs against clinically relevant drug-resistant pathogens and human gut commensals both in vitro and in vivo. A total of 79 peptides were active, with 63 targeting pathogens. These active AMPs exhibited antibacterial activity by disrupting bacterial membranes. In conclusion, our approach identified nearly one million prokaryotic AMP sequences, an open-access resource for antibiotic discovery.
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Affiliation(s)
- Célio Dias Santos-Júnior
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China; Laboratory of Microbial Processes & Biodiversity - LMPB, Department of Hydrobiology, Universidade Federal de São Carlos - UFSCar, São Carlos, São Paulo 13565-905, Brazil
| | - Marcelo D T Torres
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA; Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Yiqian Duan
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Álvaro Rodríguez Del Río
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Campus de Montegancedo-UPM, Pozuelo de Alarcón, 28223 Madrid, Spain
| | - Thomas S B Schmidt
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; APC Microbiome & School of Medicine, University College Cork, Cork, Ireland
| | - Hui Chong
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Anthony Fullam
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Michael Kuhn
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Chengkai Zhu
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Amy Houseman
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Jelena Somborski
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Anna Vines
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China; Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China; State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, China; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; Max Delbrück Centre for Molecular Medicine, Berlin, Germany; Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Campus de Montegancedo-UPM, Pozuelo de Alarcón, 28223 Madrid, Spain
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA; Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
| | - Luis Pedro Coelho
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China; Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology, Translational Research Institute, Woolloongabba, QLD, Australia.
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7
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Ma X, Aminov R, Franco OL, de la Fuente-Nunez C, Wang G, Wang J. Editorial: Antimicrobial peptides and their druggability, bio-safety, stability, and resistance. Front Microbiol 2024; 15:1425952. [PMID: 38846567 PMCID: PMC11154904 DOI: 10.3389/fmicb.2024.1425952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 05/14/2024] [Indexed: 06/09/2024] Open
Affiliation(s)
- Xuanxuan Ma
- Innovative Team of Antimicrobial Peptides and Alternatives to Antibiotics, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
- Gene Engineering Laboratory, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Rustam Aminov
- The School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, United Kingdom
| | - Octavio Luiz Franco
- S-Inova Biotech, Universidade Católica Dom Bosco, Campo Grande, MS, Brazil
- Centro de Análises Proteômicas e Bioquímicas Programa de Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, DF, Brazil
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Perelman School of Medicine, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, United States
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, United States
| | - Guangshun Wang
- Department of Pathology, Microbiology, and Immunology, University of Nebraska Medical Center, Omaha, NE, United States
| | - Jianhua Wang
- Innovative Team of Antimicrobial Peptides and Alternatives to Antibiotics, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
- Gene Engineering Laboratory, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing, China
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8
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Mancuso CP, Baker JS, Qu E, Tripp AD, Balogun IO, Lieberman TD. Intraspecies warfare restricts strain coexistence in human skin microbiomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.07.592803. [PMID: 38765968 PMCID: PMC11100718 DOI: 10.1101/2024.05.07.592803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Determining why only a fraction of encountered or applied bacterial strains engraft in a given person's microbiome is crucial for understanding and engineering these communities1. Previous work has established that metabolism can determine colonization success in vivo2-4, but relevance of bacterial warfare in preventing engraftment has been less explored. Here, we demonstrate that intraspecies warfare presents a significant barrier to strain transmission in the skin microbiome by profiling 14,884 pairwise interactions between Staphylococcus epidermidis cultured from eighteen human subjects from six families. We find that intraspecies antagonisms are abundant; these interactions are mechanistically diverse, independent of the relatedness between strains, and consistent with rapid evolution via horizontal gene transfer. Ability to antagonize more strains is associated with reaching a higher fraction of the on-person S. epidermidis community. Moreover, antagonisms are significantly depleted among strains residing on the same person relative to random assemblages. Two notable exceptions, in which bacteria evolved to become sensitive to antimicrobials found on the same host, are explained by mutations that provide phage resistance, contextualizing the importance of warfare among other lethal selective pressures. Taken together, our results emphasize that accounting for intraspecies bacterial warfare is essential to the design of long-lasting probiotic therapeutics.
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Affiliation(s)
- Christopher P. Mancuso
- Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology; Cambridge, MA 02142, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology; Cambridge, MA 02142, USA
| | - Jacob S. Baker
- Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology; Cambridge, MA 02142, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology; Cambridge, MA 02142, USA
| | - Evan Qu
- Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology; Cambridge, MA 02142, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology; Cambridge, MA 02142, USA
| | - A. Delphine Tripp
- Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology; Cambridge, MA 02142, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology; Cambridge, MA 02142, USA
- Department of Systems Biology, Harvard University; Cambridge, MA 02138, USA
| | - Ishaq O. Balogun
- Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology; Cambridge, MA 02142, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology; Cambridge, MA 02142, USA
| | - Tami D. Lieberman
- Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology; Cambridge, MA 02142, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology; Cambridge, MA 02142, USA
- Broad Institute of MIT and Harvard; Cambridge, MA 02142, USA
- Ragon Institute of MGH, MIT, and Harvard; Cambridge, MA 02142, USA
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9
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Yang R, Xue Z, Li X, Xu T, Zhong Y, Hu S, Qin S, Guo Y. Novel natural osthole-inspired amphiphiles as membrane targeting antibacterials against methicillin-resistant Staphylococcus aureus (MRSA). Eur J Med Chem 2024; 271:116449. [PMID: 38691893 DOI: 10.1016/j.ejmech.2024.116449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 04/23/2024] [Accepted: 04/23/2024] [Indexed: 05/03/2024]
Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) is a widespread pathogen causing clinical infections and is multi-resistant to many antibiotics, making it urgent need to develop novel antibacterials to combat MRSA. Herein, we designed and prepared a series of novel osthole amphiphiles 6a-6ad by mimicking the structures and function of antimicrobial peptides (AMPs). Antibacterial assays showed that osthole amphiphile 6aa strongly inhibited S. aureus and 10 clinical MRSA isolates with MIC values of 1-2 μg/mL, comparable to that of the commercial antibiotic vancomycin. Additionally, 6aa had the advantages of rapid bacteria killing without readily developing drug resistance, low toxicity, good membrane selectivity, and good plasma stability. Mechanistic studies indicated that 6aa possesses good membrane-targeting ability to bind to phosphatidylglycerol (PG) on the bacterial cell membranes, thereby disrupting the cell membranes and causing an increase in intracellular ROS as well as leakage of proteins and DNA, and accelerating bacterial death. Notably, in vivo activity results revealed that 6aa exhibits strong anti-MRSA efficacy than vancomycin as well as a substantial reduction in MRSA-induced proinflammatory cytokines, including TNF-α and IL-6. Given the impressive in vitro and in vivo anti-MRSA efficacy of 6aa, which makes it a potential candidate against MRSA infections.
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Affiliation(s)
- Ruige Yang
- Hunan Province Cooperative Innovation Center for Molecular Target New Drug Study, School of Pharmaceutical Science, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan Province, China; School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450001, Henan Province, China.
| | - Zihan Xue
- School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450001, Henan Province, China
| | - Xinhui Li
- School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450001, Henan Province, China
| | - Ting Xu
- Hunan Province Cooperative Innovation Center for Molecular Target New Drug Study, School of Pharmaceutical Science, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan Province, China
| | - Yan Zhong
- Hunan Province Cooperative Innovation Center for Molecular Target New Drug Study, School of Pharmaceutical Science, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan Province, China
| | - Songlin Hu
- Hunan Province Cooperative Innovation Center for Molecular Target New Drug Study, School of Pharmaceutical Science, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan Province, China
| | - Shangshang Qin
- School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450001, Henan Province, China
| | - Yong Guo
- Hunan Province Cooperative Innovation Center for Molecular Target New Drug Study, School of Pharmaceutical Science, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan Province, China; School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450001, Henan Province, China.
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10
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Torres MDT, Cesaro A, de la Fuente-Nunez C. Peptides from non-immune proteins target infections through antimicrobial and immunomodulatory properties. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.25.586636. [PMID: 38585860 PMCID: PMC10996515 DOI: 10.1101/2024.03.25.586636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Encrypted peptides have been recently described as a new class of antimicrobial molecules. They have been proposed to play a role in host immunity and as alternatives to conventional antibiotics. Intriguingly, many of these peptides are found embedded in proteins unrelated to the immune system, suggesting that immunological responses may extend beyond traditional host immunity proteins. To test this idea, here we synthesized and tested representative peptides derived from non-immune proteins for their ability to exert antimicrobial and immunomodulatory properties. Our experiments revealed that most of the tested peptides from non-immune proteins, derived from structural proteins as well as proteins from the nervous and visual systems, displayed potent in vitro antimicrobial activity. These molecules killed bacterial pathogens by targeting their membrane, and those originating from the same region of the body exhibited synergistic effects when combined. Beyond their antimicrobial properties, nearly 90% of the peptides tested exhibited immunomodulatory effects, modulating inflammatory mediators such as IL-6, TNF-α, and MCP-1. Moreover, eight of the peptides identified, collagenin 3 and 4, zipperin-1 and 2, and immunosin-2, 3, 12, and 13, displayed anti-infective efficacy in two different preclinical mouse models, reducing bacterial infections by up to four orders of magnitude. Altogether, our results support the hypothesis that peptides from non-immune proteins may play a role in host immunity. These results potentially expand our notion of the immune system to include previously unrecognized proteins and peptides that may be activated upon infection to confer protection to the host.
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11
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De los Santos L, Beckman RL, DeBarro C, Keener JE, Torres MD, de la Fuente-Nunez C, Brodbelt JS, Fleeman RM. Polyproline peptide targets Klebsiella pneumoniae polysaccharides to collapse biofilms. CELL REPORTS. PHYSICAL SCIENCE 2024; 5:101869. [PMID: 38605913 PMCID: PMC11008256 DOI: 10.1016/j.xcrp.2024.101869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
Hypervirulent Klebsiella pneumoniae is known for its increased extracellular polysaccharide production. Biofilm matrices of hypervirulent K. pneumoniae have increased polysaccharide abundance and are uniquely susceptible to disruption by peptide bactenecin 7 (bac7 (1-35)). Here, using confocal microscopy, we show that polysaccharides within the biofilm matrix collapse following bac7 (1-35) treatment. This collapse led to the release of cells from the biofilm, which were then killed by the peptide. Characterization of truncated peptide analogs revealed that their interactions with polysaccharide were responsible for the biofilm matrix changes that accompany bac7 (1-35) treatment. Ultraviolet photodissociation mass spectrometry with the parental peptide or a truncated analog bac7 (10-35) reveal the important regions for bac7 (1-35) complexing with polysaccharides. Finally, we tested bac7 (1-35) using a murine skin abscess model and observed a significant decrease in the bacterial burden. These findings unveil the potential of bac7 (1-35) polysaccharide interactions to collapse K. pneumoniae biofilms.
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Affiliation(s)
- Laura De los Santos
- Division of Immunity and Pathogenesis, Burnett School of Biomedical Sciences, College of Medicine, University of Central Florida, Orlando, FL 32827, USA
| | - Robert L. Beckman
- Division of Immunity and Pathogenesis, Burnett School of Biomedical Sciences, College of Medicine, University of Central Florida, Orlando, FL 32827, USA
| | - Christina DeBarro
- Division of Immunity and Pathogenesis, Burnett School of Biomedical Sciences, College of Medicine, University of Central Florida, Orlando, FL 32827, USA
| | - James E. Keener
- Department of Chemistry, The University of Texas at Austin, Austin, TX 78712, USA
| | - Marcelo D.T. Torres
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jennifer S. Brodbelt
- Department of Chemistry, The University of Texas at Austin, Austin, TX 78712, USA
| | - Renee M. Fleeman
- Division of Immunity and Pathogenesis, Burnett School of Biomedical Sciences, College of Medicine, University of Central Florida, Orlando, FL 32827, USA
- X (formerly Twitter): @FleemanLab
- Lead contact
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12
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Nazarian-Firouzabadi F, Torres MDT, de la Fuente-Nunez C. Recombinant production of antimicrobial peptides in plants. Biotechnol Adv 2024; 71:108296. [PMID: 38042311 DOI: 10.1016/j.biotechadv.2023.108296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 11/10/2023] [Accepted: 11/26/2023] [Indexed: 12/04/2023]
Abstract
Classical plant breeding methods are limited in their ability to confer disease resistance on plants. However, in recent years, advancements in molecular breeding and biotechnological have provided new approaches to overcome these limitations and protect plants from disease. Antimicrobial peptides (AMPs) constitute promising agents that may be able to protect against infectious agents. Recently, peptides have been recombinantly produced in plants at scale and low cost. Because AMPs are less likely than conventional antimicrobials to elicit resistance of pathogenic bacteria, they open up exciting new avenues for agricultural applications. Here, we review recent advances in the design and production of bioactive recombinant AMPs that can effectively protect crop plants from diseases.
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Affiliation(s)
- Farhad Nazarian-Firouzabadi
- Production Engineering and Plant Genetics Department, Faculty of Agriculture, Lorestan University, P.O. Box, 465, Khorramabad, Iran.
| | - Marcelo Der Torossian Torres
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States of America; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States of America; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, United States of America.
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13
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de la Fuente-Nunez C. AI in infectious diseases: The role of datasets. Drug Resist Updat 2024; 73:101067. [PMID: 38387282 DOI: 10.1016/j.drup.2024.101067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 02/07/2024] [Accepted: 02/08/2024] [Indexed: 02/24/2024]
Affiliation(s)
- Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States of America; Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, United States of America; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, United States of America.
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14
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Hemmati S, Saeidikia Z, Seradj H, Mohagheghzadeh A. Immunomodulatory Peptides as Vaccine Adjuvants and Antimicrobial Agents. Pharmaceuticals (Basel) 2024; 17:201. [PMID: 38399416 PMCID: PMC10892805 DOI: 10.3390/ph17020201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Revised: 01/26/2024] [Accepted: 01/28/2024] [Indexed: 02/25/2024] Open
Abstract
The underdevelopment of adjuvant discovery and diversity, compared to core vaccine technology, is evident. On the other hand, antibiotic resistance is on the list of the top ten threats to global health. Immunomodulatory peptides that target a pathogen and modulate the immune system simultaneously are promising for the development of preventive and therapeutic molecules. Since investigating innate immunity in insects has led to prominent achievements in human immunology, such as toll-like receptor (TLR) discovery, we used the capacity of the immunomodulatory peptides of arthropods with concomitant antimicrobial or antitumor activity. An SVM-based machine learning classifier identified short immunomodulatory sequences encrypted in 643 antimicrobial peptides from 55 foe-to-friend arthropods. The critical features involved in efficacy and safety were calculated. Finally, 76 safe immunomodulators were identified. Then, molecular docking and simulation studies defined the target of the most optimal peptide ligands among all human cell-surface TLRs. SPalf2-453 from a crab is a cell-penetrating immunoadjuvant with antiviral properties. The peptide interacts with the TLR1/2 heterodimer. SBsib-711 from a blackfly is a TLR4/MD2 ligand used as a cancer vaccine immunoadjuvant. In addition, SBsib-711 binds CD47 and PD-L1 on tumor cells, which is applicable in cancer immunotherapy as a checkpoint inhibitor. MRh4-679 from a shrimp is a broad-spectrum or universal immunoadjuvant with a putative Th1/Th2-balanced response. We also implemented a pathway enrichment analysis to define fingerprints or immunological signatures for further in vitro and in vivo immunogenicity and reactogenicity measurements. Conclusively, combinatorial machine learning, molecular docking, and simulation studies, as well as systems biology, open a new opportunity for the discovery and development of multifunctional prophylactic and therapeutic lead peptides.
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Affiliation(s)
- Shiva Hemmati
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz 71345-1583, Iran
- Biotechnology Research Center, Shiraz University of Medical Sciences, Shiraz 71345-1583, Iran
- Department of Pharmaceutical Biology, Faculty of Pharmaceutical Sciences, UCSI University, Cheras, Kuala Lumpur 56000, Malaysia
| | - Zahra Saeidikia
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz 71345-1583, Iran;
| | - Hassan Seradj
- Department of Medicinal Chemistry, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz 71345-1583, Iran;
| | - Abdolali Mohagheghzadeh
- Department of Phytopharmaceuticals, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz 71345-1583, Iran;
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15
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Liu GY, Yu D, Fan MM, Zhang X, Jin ZY, Tang C, Liu XF. Antimicrobial resistance crisis: could artificial intelligence be the solution? Mil Med Res 2024; 11:7. [PMID: 38254241 PMCID: PMC10804841 DOI: 10.1186/s40779-024-00510-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
Antimicrobial resistance is a global public health threat, and the World Health Organization (WHO) has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed. The discovery and introduction of novel antibiotics are time-consuming and expensive. According to WHO's report of antibacterial agents in clinical development, only 18 novel antibiotics have been approved since 2014. Therefore, novel antibiotics are critically needed. Artificial intelligence (AI) has been rapidly applied to drug development since its recent technical breakthrough and has dramatically improved the efficiency of the discovery of novel antibiotics. Here, we first summarized recently marketed novel antibiotics, and antibiotic candidates in clinical development. In addition, we systematically reviewed the involvement of AI in antibacterial drug development and utilization, including small molecules, antimicrobial peptides, phage therapy, essential oils, as well as resistance mechanism prediction, and antibiotic stewardship.
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Affiliation(s)
- Guang-Yu Liu
- Department of Immunology and Pathogen Biology, School of Basic Medical Sciences, Hangzhou Normal University, Key Laboratory of Aging and Cancer Biology of Zhejiang Province, Key Laboratory of Inflammation and Immunoregulation of Hangzhou, Hangzhou Normal University, Hangzhou, 311121, China
| | - Dan Yu
- National Key Discipline of Pediatrics Key Laboratory of Major Diseases in Children Ministry of Education, Laboratory of Dermatology, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Mei-Mei Fan
- Department of Immunology and Pathogen Biology, School of Basic Medical Sciences, Hangzhou Normal University, Key Laboratory of Aging and Cancer Biology of Zhejiang Province, Key Laboratory of Inflammation and Immunoregulation of Hangzhou, Hangzhou Normal University, Hangzhou, 311121, China
| | - Xu Zhang
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, 55905, USA
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Ze-Yu Jin
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Christoph Tang
- Sir William Dunn School of Pathology, University of Oxford, Oxford, OX1 3RE, UK.
| | - Xiao-Fen Liu
- Institute of Antibiotics, Huashan Hospital, Fudan University, Key Laboratory of Clinical Pharmacology of Antibiotics, National Health Commission of the People's Republic of China, National Clinical Research Centre for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China.
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16
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Rayfield KM, Mychajliw AM, Singleton RR, Sholts SB, Hofman CA. Uncovering the Holocene roots of contemporary disease-scapes: bringing archaeology into One Health. Proc Biol Sci 2023; 290:20230525. [PMID: 38052246 DOI: 10.1098/rspb.2023.0525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 11/06/2023] [Indexed: 12/07/2023] Open
Abstract
The accelerating pace of emerging zoonotic diseases in the twenty-first century has motivated cross-disciplinary collaboration on One Health approaches, combining microbiology, veterinary and environmental sciences, and epidemiology for outbreak prevention and mitigation. Such outbreaks are often caused by spillovers attributed to human activities that encroach on wildlife habitats and ecosystems, such as land use change, industrialized food production, urbanization and animal trade. While the origin of anthropogenic effects on animal ecology and biogeography can be traced to the Late Pleistocene, the archaeological record-a long-term archive of human-animal-environmental interactions-has largely been untapped in these One Health approaches, thus limiting our understanding of these dynamics over time. In this review, we examine how humans, as niche constructors, have facilitated new host species and 'disease-scapes' from the Late Pleistocene to the Anthropocene, by viewing zooarchaeological, bioarchaeological and palaeoecological data with a One Health perspective. We also highlight how new biomolecular tools and advances in the '-omics' can be holistically coupled with archaeological and palaeoecological reconstructions in the service of studying zoonotic disease emergence and re-emergence.
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Affiliation(s)
- Kristen M Rayfield
- Department of Ecology and Evolution, Stony Brook University, Stony Brook, NY, USA
- Laboratories of Molecular Anthropology & Microbiome Research, University of Oklahoma, Norman, OK, USA
- Department of Anthropology, University of Oklahoma, Norman, OK 73019-0390, USA
- Department of Anthropology, National Museum of Natural History, Smithsonian Institution, Washington, DC, USA
| | - Alexis M Mychajliw
- Laboratories of Molecular Anthropology & Microbiome Research, University of Oklahoma, Norman, OK, USA
- Department of Anthropology, University of Oklahoma, Norman, OK 73019-0390, USA
- Department of Anthropology, National Museum of Natural History, Smithsonian Institution, Washington, DC, USA
- Department of Biology & Program in Environmental Studies, Middlebury College, Middlebury, VT 05753-6203, USA
| | - Robin R Singleton
- Laboratories of Molecular Anthropology & Microbiome Research, University of Oklahoma, Norman, OK, USA
- Department of Anthropology, University of Oklahoma, Norman, OK 73019-0390, USA
| | - Sabrina B Sholts
- Department of Anthropology, National Museum of Natural History, Smithsonian Institution, Washington, DC, USA
| | - Courtney A Hofman
- Laboratories of Molecular Anthropology & Microbiome Research, University of Oklahoma, Norman, OK, USA
- Department of Anthropology, University of Oklahoma, Norman, OK 73019-0390, USA
- Department of Anthropology, National Museum of Natural History, Smithsonian Institution, Washington, DC, USA
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17
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Lee YCJ, Javdan B. Ice Cores as a Source for Antimicrobials: From Bioprospecting to Biodesign. BIODESIGN RESEARCH 2023; 5:0024. [PMID: 37928441 PMCID: PMC10623340 DOI: 10.34133/bdr.0024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 10/21/2023] [Indexed: 11/07/2023] Open
Abstract
The golden age has passed for antibiotic discovery, and while some antibiotics are currently in various phases of clinical trials in the United States, many pharmaceutical companies have abandoned antibiotic research. With the need for antibiotics, we should expand our horizon for therapeutic mining and can look toward understudied sources such as ice cores. Ice cores contain microorganisms and genetic material that have been frozen in time for thousands of years. The antibiotics used by these organisms are encoded in their genomes, which can be unlocked, identified, and characterized with modern advances in molecular biology, genetic sequencing, various computational approaches, and established natural product discovery pipelines. While synthetic biology can be used in natural product discovery approaches, synthetic biology and bioengineering efforts can also be leveraged in the selection and biodesign of increased compound yields, potency, and stability. Here, we provide the perspective that ice cores can be a source of novel antibiotic compounds and that the tools of synthetic biology can be used to design better antimicrobials.
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Affiliation(s)
| | - Bahar Javdan
- Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA
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18
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Cesaro A, de la Fuente-Nunez C. Antibiotic identified by AI. Nat Chem Biol 2023; 19:1296-1298. [PMID: 37821719 DOI: 10.1038/s41589-023-01448-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Affiliation(s)
- Angela Cesaro
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
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de la Fuente-Nunez C, Cesaro A, Hancock REW. Antibiotic failure: Beyond antimicrobial resistance. Drug Resist Updat 2023; 71:101012. [PMID: 37924726 DOI: 10.1016/j.drup.2023.101012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/13/2023] [Accepted: 10/16/2023] [Indexed: 11/06/2023]
Abstract
Despite significant progress in antibiotic discovery, millions of lives are lost annually to infections. Surprisingly, the failure of antimicrobial treatments to effectively eliminate pathogens frequently cannot be attributed to genetically-encoded antibiotic resistance. This review aims to shed light on the fundamental mechanisms contributing to clinical scenarios where antimicrobial therapies are ineffective (i.e., antibiotic failure), emphasizing critical factors impacting this under-recognized issue. Explored aspects include biofilm formation and sepsis, as well as the underlying microbiome. Therapeutic strategies beyond antibiotics, are examined to address the dimensions and resolution of antibiotic failure, actively contributing to this persistent but escalating crisis. We discuss the clinical relevance of antibiotic failure beyond resistance, limited availability of therapies, potential of new antibiotics to be ineffective, and the urgent need for novel anti-infectives or host-directed therapies directly addressing antibiotic failure. Particularly noteworthy is multidrug adaptive resistance in biofilms that represent 65 % of infections, due to the lack of approved therapies. Sepsis, responsible for 19.7 % of all deaths (as well as severe COVID-19 deaths), is a further manifestation of this issue, since antibiotics are the primary frontline therapy, and yet 23 % of patients succumb to this condition.
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Affiliation(s)
- Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
| | - Angela Cesaro
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Robert E W Hancock
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, Canada.
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20
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Pedron CN, Torres MDT, Oliveira CS, Silva AF, Andrade GP, Wang Y, Pinhal MAS, Cerchiaro G, da Silva Junior PI, da Silva FD, Radhakrishnan R, de la Fuente-Nunez C, Oliveira Junior VX. Molecular hybridization strategy for tuning bioactive peptide function. Commun Biol 2023; 6:1067. [PMID: 37857855 PMCID: PMC10587126 DOI: 10.1038/s42003-023-05254-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 08/17/2023] [Indexed: 10/21/2023] Open
Abstract
The physicochemical and structural properties of antimicrobial peptides (AMPs) determine their mechanism of action and biological function. However, the development of AMPs as therapeutic drugs has been traditionally limited by their toxicity for human cells. Tuning the physicochemical properties of such molecules may abolish toxicity and yield synthetic molecules displaying optimal safety profiles and enhanced antimicrobial activity. Here, natural peptides were modified to improve their activity by the hybridization of sequences from two different active peptide sequences. Hybrid AMPs (hAMPs) were generated by combining the amphipathic faces of the highly toxic peptide VmCT1, derived from scorpion venom, with parts of four other naturally occurring peptides having high antimicrobial activity and low toxicity against human cells. This strategy led to the design of seven synthetic bioactive variants, all of which preserved their structure and presented increased antimicrobial activity (3.1-128 μmol L-1). Five of the peptides (three being hAMPs) presented high antiplasmodial at 0.8 μmol L-1, and virtually no undesired toxic effects against red blood cells. In sum, we demonstrate that peptide hybridization is an effective strategy for redirecting biological activity to generate novel bioactive molecules with desired properties.
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Affiliation(s)
- Cibele Nicolaski Pedron
- Centro de Ciências Naturais e Humanas, Universidade Federal do ABC, Santo André, SP, 09210580, Brazil
- Departamento de Bioquímica, Universidade Federal de São Paulo, São Paulo, SP, 04044020, Brazil
| | - Marcelo Der Torossian Torres
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Cyntia Silva Oliveira
- Departamento de Bioquímica, Universidade Federal de São Paulo, São Paulo, SP, 04044020, Brazil
| | - Adriana Farias Silva
- Departamento de Biofísica, Universidade Federal de São Paulo, São Paulo, SP, 04044020, Brazil
| | - Gislaine Patricia Andrade
- Centro de Ciências Naturais e Humanas, Universidade Federal do ABC, Santo André, SP, 09210580, Brazil
- Departamento de Biofísica, Universidade Federal de São Paulo, São Paulo, SP, 04044020, Brazil
| | - Yiming Wang
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Giselle Cerchiaro
- Centro de Ciências Naturais e Humanas, Universidade Federal do ABC, Santo André, SP, 09210580, Brazil
| | | | - Fernanda Dias da Silva
- Centro de Ciências Naturais e Humanas, Universidade Federal do ABC, Santo André, SP, 09210580, Brazil
| | - Ravi Radhakrishnan
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
| | - Vani Xavier Oliveira Junior
- Centro de Ciências Naturais e Humanas, Universidade Federal do ABC, Santo André, SP, 09210580, Brazil.
- Departamento de Bioquímica, Universidade Federal de São Paulo, São Paulo, SP, 04044020, Brazil.
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21
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Bravo A, Moreno-Blanco A, Espinosa M. One Earth: The Equilibrium between the Human and the Bacterial Worlds. Int J Mol Sci 2023; 24:15047. [PMID: 37894729 PMCID: PMC10606248 DOI: 10.3390/ijms242015047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 09/21/2023] [Accepted: 09/25/2023] [Indexed: 10/29/2023] Open
Abstract
Misuse and abuse of antibiotics on humans, cattle, and crops have led to the selection of multi-resistant pathogenic bacteria, the most feared 'superbugs'. Infections caused by superbugs are progressively difficult to treat, with a subsequent increase in lethality: the toll on human lives is predicted to reach 10 million by 2050. Here we review three concepts linked to the growing resistance to antibiotics, namely (i) the Resistome, which refers to the collection of bacterial genes that confer resistance to antibiotics, (ii) the Mobilome, which includes all the mobile genetic elements that participate in the spreading of antibiotic resistance among bacteria by horizontal gene transfer processes, and (iii) the Nichome, which refers to the set of genes that are expressed when bacteria try to colonize new niches. We also discuss the strategies that can be used to tackle bacterial infections and propose an entente cordiale with the bacterial world so that instead of war and destruction of the 'fierce enemy' we can achieve a peaceful coexistence (the One Earth concept) between the human and the bacterial worlds. This, in turn, will contribute to microbial biodiversity, which is crucial in a globally changing climate due to anthropogenic activities.
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Affiliation(s)
- Alicia Bravo
- Centro de Investigaciones Biológicas Margarita Salas, Consejo Superior de Investigaciones Científicas (CSIC), Ramiro de Maeztu 9, E-28040 Madrid, Spain
| | | | - Manuel Espinosa
- Centro de Investigaciones Biológicas Margarita Salas, Consejo Superior de Investigaciones Científicas (CSIC), Ramiro de Maeztu 9, E-28040 Madrid, Spain
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22
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Wang G. The antimicrobial peptide database is 20 years old: Recent developments and future directions. Protein Sci 2023; 32:e4778. [PMID: 37695921 PMCID: PMC10535814 DOI: 10.1002/pro.4778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 09/05/2023] [Accepted: 09/07/2023] [Indexed: 09/13/2023]
Abstract
In 2023, the Antimicrobial Peptide Database (currently available at https://aps.unmc.edu) is 20-years-old. The timeline for the APD expansion in peptide entries, classification methods, search functions, post-translational modifications, binding targets, and mechanisms of action of antimicrobial peptides (AMPs) has been summarized in our previous Protein Science paper. This article highlights new database additions and findings. To facilitate antimicrobial development to combat drug-resistant pathogens, the APD has been re-annotating the data for antibacterial activity (active, inactive, and uncertain), toxicity (hemolytic and nonhemolytic AMPs), and salt tolerance (salt sensitive and insensitive). Comparison of the respective desired and undesired AMP groups produces new knowledge for peptide design. Our unification of AMPs from the six life kingdoms into "natural AMPs" enabled the first comparison with globular or transmembrane proteins. Due to the dominance of amphipathic helical and disulfide-linked peptides, cysteine, glycine, and lysine in natural AMPs are much more abundant than those in globular proteins. To include peptides predicted by machine learning, a new "predicted" group has been created. Remarkably, the averaged amino acid composition of predicted peptides is located between the lower bound of natural AMPs and the upper bound of synthetic peptides. Synthetic peptides in the current APD, with the highest cationic and hydrophobic amino acid percentages, are mostly designed with varying degrees of optimization. Hence, natural AMPs accumulated in the APD over 20 years have laid the foundation for machine learning prediction. We discuss future directions for peptide discovery. It is anticipated that the APD will continue to play a role in research and education.
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Affiliation(s)
- Guangshun Wang
- Department of Pathology and Microbiology, College of MedicineUniversity of Nebraska Medical CenterOmahaNebraskaUSA
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23
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Torres MDT, Brooks E, Cesaro A, Sberro H, Nicolaou C, Bhatt AS, de la Fuente-Nunez C. Human gut metagenomic mining reveals an untapped source of peptide antibiotics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.31.555711. [PMID: 37693399 PMCID: PMC10491270 DOI: 10.1101/2023.08.31.555711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Drug-resistant bacteria are outpacing traditional antibiotic discovery efforts. Here, we computationally mined 444,054 families of putative small proteins from 1,773 human gut metagenomes, identifying 323 peptide antibiotics encoded in small open reading frames (smORFs). To test our computational predictions, 78 peptides were synthesized and screened for antimicrobial activity in vitro, with 59% displaying activity against either pathogens or commensals. Since these peptides were unique compared to previously reported antimicrobial peptides, we termed them smORF-encoded peptides (SEPs). SEPs killed bacteria by targeting their membrane, synergized with each other, and modulated gut commensals, indicating that they may play a role in reconfiguring microbiome communities in addition to counteracting pathogens. The lead candidates were anti-infective in both murine skin abscess and deep thigh infection models. Notably, prevotellin-2 from Prevotella copri presented activity comparable to the commonly used antibiotic polymyxin B. We report the discovery of hundreds of peptide sequences in the human gut.
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Affiliation(s)
- Marcelo D. T. Torres
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States of America
| | - Erin Brooks
- Department of Medicine (Hematology; Blood and Marrow Transplantation), Stanford University, Stanford, CA, United States of America
| | - Angela Cesaro
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States of America
| | - Hila Sberro
- Department of Medicine (Hematology; Blood and Marrow Transplantation), Stanford University, Stanford, CA, United States of America
| | - Cosmos Nicolaou
- Department of Medicine (Hematology; Blood and Marrow Transplantation), Stanford University, Stanford, CA, United States of America
| | - Ami S. Bhatt
- Department of Medicine (Hematology; Blood and Marrow Transplantation), Stanford University, Stanford, CA, United States of America
- Department of Genetics, Stanford University, Stanford, CA, United States of America
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States of America
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Wang J. Seeking old wisdoms for new AMP discovery. Cell Host Microbe 2023; 31:1251-1253. [PMID: 37562358 DOI: 10.1016/j.chom.2023.06.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 06/30/2023] [Indexed: 08/12/2023]
Abstract
In this issue of Cell Host & Microbe, Maasch et al. built a deep-learning model for proteolytic site prediction and applied this approach to modern and extinct human proteomes, revealing antimicrobial peptides. The de-extinction of archaic encrypted peptides provides a revolutionary approach for discovering functional peptides with therapeutic potential.
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Affiliation(s)
- Jun Wang
- CAS Key Lab for Pathogenic Microbiology and Immunology, Institute of Microbiology, CAS, Beijing 100101, China.
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25
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Sidik S. AI search of Neanderthal proteins resurrects 'extinct' antibiotics. Nature 2023:10.1038/d41586-023-02403-0. [PMID: 37507506 DOI: 10.1038/d41586-023-02403-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2023]
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Cesaro A, Bagheri M, Torres MDT, Wan F, de la Fuente-Nunez C. Deep learning tools to accelerate antibiotic discovery. Expert Opin Drug Discov 2023; 18:1245-1257. [PMID: 37794737 PMCID: PMC10790350 DOI: 10.1080/17460441.2023.2250721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 08/18/2023] [Indexed: 10/06/2023]
Abstract
INTRODUCTION As machine learning (ML) and artificial intelligence (AI) expand to many segments of our society, they are increasingly being used for drug discovery. Recent deep learning models offer an efficient way to explore high-dimensional data and design compounds with desired properties, including those with antibacterial activity. AREAS COVERED This review covers key frameworks in antibiotic discovery, highlighting physicochemical features and addressing dataset limitations. The deep learning approaches here described include discriminative models such as convolutional neural networks, recurrent neural networks, graph neural networks, and generative models like neural language models, variational autoencoders, generative adversarial networks, normalizing flow, and diffusion models. As the integration of these approaches in drug discovery continues to evolve, this review aims to provide insights into promising prospects and challenges that lie ahead in harnessing such technologies for the development of antibiotics. EXPERT OPINION Accurate antimicrobial prediction using deep learning faces challenges such as imbalanced data, limited datasets, experimental validation, target strains, and structure. The integration of deep generative models with bioinformatics, molecular dynamics, and data augmentation holds the potential to overcome these challenges, enhance model performance, and utlimately accelerate antimicrobial discovery.
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Affiliation(s)
- Angela Cesaro
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Mojtaba Bagheri
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Marcelo D. T. Torres
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Fangping Wan
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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